Abstract: Network analytic methods that are ubiquitous in other areas, such as systems neuroscience,
have recently been used to test network theories in psychology, including intelligence research.
The network or mutualism theory of intelligence proposes that the statistical associations among
cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations
among them throughout development. In this study, we used network models (specifically LASSO)
of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model
brain–behavior relationships essential for general intelligence in a large (behavioral, N = 805; cortical
volume, N = 246; fractional anisotropy, N = 165) developmental (ages 5–18) cohort of struggling
learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive,
neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and
calculating node centrality (absolute strength and bridge strength), we found convergent evidence
that subsets of both cognitive and neural nodes play an intermediary role ‘between’ brain and
behavior. We discuss implications and possible avenues for future studies.
Keywords: general intelligence; cortical volume; fractional anisotropy; brain structural covariance;
cognitive network neuroscience; multilayer network analysis